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93463cac-471a-469d-ad52-0514fd9b67f2

Detection and attribution of climate change using deep learning

https://github.com/eds-book/93463cac-471a-469d-ad52-0514fd9b67f2

Science Score: 67.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 2 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: zenodo.org
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (10.6%) to scientific vocabulary

Keywords

climate-change environmental-data-science modelling reproducibility-challenge
Last synced: 6 months ago · JSON representation ·

Repository

Detection and attribution of climate change using deep learning

Basic Info
  • Host: GitHub
  • Owner: eds-book
  • License: other
  • Language: Jupyter Notebook
  • Default Branch: main
  • Homepage:
  • Size: 18.6 MB
Statistics
  • Stars: 4
  • Watchers: 2
  • Forks: 2
  • Open Issues: 4
  • Releases: 7
Topics
climate-change environmental-data-science modelling reproducibility-challenge
Created almost 3 years ago · Last pushed 6 months ago
Metadata Files
Readme License Citation

README.md

Deep learning and variational inversion to quantify and attribute climate change (CIRC23)

Continuous integration badge Binder doi notebook review

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How to run

Running locally

You may also download the notebook from GitHub to run it locally: 1. Open your terminal

  1. Check your conda install with conda --version. If you don't have conda, install it by following these instructions (see here)

  2. Clone the repository bash git clone https://github.com/eds-book-gallery/93463cac-471a-469d-ad52-0514fd9b67f2.git

  3. Move into the cloned repository bash cd 93463cac-471a-469d-ad52-0514fd9b67f2

  4. Create and activate your environment from the .binder/environment.yml file bash conda env create -f .binder/environment.yml conda activate 93463cac-471a-469d-ad52-0514fd9b67f2

  5. Launch the jupyter interface of your preference, notebook, jupyter notebook or lab jupyter lab

Owner

  • Name: Environmental Data Science Book
  • Login: eds-book
  • Kind: organization
  • Email: environmental.ds.book@gmail.com

Organisation repo of EDS book for governance, outreach and other community-led activities

Citation (CITATION.cff) 

cff-version: 1.2.0
message: Please cite the following works when using this project.
abstract: >-
  Notebook developed to demonstrate the computational reproduction of the paper
  Detection and attribution of climate change: A deep learning and variational
  approach, published in Environmental Data Science journal.
title: >-
  Deep learning and variational inversion to quantify and attribute climate
  change (Jupyter Notebook) published in the Environmental Data Science book
authors:
  - family-names: Domazetoski
    given-names: Viktor
    affiliation: University of Göttingen
    orcid: 0000-0001-9830-7032
    email: viktor.domazetoski@hotmail.com
  - family-names: Zúñiga-González
    given-names: Andrés
    affiliation: University of Cambridge
  - family-names: Allemang
    given-names: Owen
    affiliation: University of Cambridge
date-released: '2024-09-13'
contact:
  - family-names: Domazetoski
    given-names: Viktor
    affiliation: University of Göttingen
    orcid: 0000-0001-9830-7032
    email: viktor.domazetoski@hotmail.com
identifiers:
  - description: Open review report for this notebook
    type: url
    value: https://github.com/eds-book/notebooks-reviews/issues/9
keywords:
  - Atmosphere
  - Modelling
  - Special Issue
  - Python
license: MIT
license-url: https://opensource.org/license/MIT
repository: https://github.com/eds-book/39d9c177-11da-41b2-9b64-63f4c1c834b3
references:
  - authors:
      - family-names: Bône
        given-names: Constantin
      - family-names: Gastineau
        given-names: Guillaume
      - family-names: Thiria
        given-names: Sylvie
      - family-names: Gallinari
        given-names: Patrick
    doi: 10.1017/eds.2022.17
    type: article
    scope: >-
      Reproduced paper as part of the 2023 Climate Informatics Reproducibility
      Challenge.
    title: >-
      Detection and attribution of climate change: A deep learning and
      variational approach
    journal: Environmental Data Science journal
    year: 2022
type: software
version: v2025.6.0

GitHub Events

Total
  • Release event: 1
  • Push event: 12
  • Pull request event: 1
  • Create event: 2
Last Year
  • Release event: 1
  • Push event: 12
  • Pull request event: 1
  • Create event: 2